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DEVELOPMENT OF ARTIFICIAL INTELLIGENCE-BASED WEATHER FORECASTING SYSTEM WITH IOT-SUPPORTED WEATHER DATA

Year 2025, Volume: 28 Issue: 1, 524 - 535, 03.03.2025

Nurullah Doğan , Fatih Özyurt

Abstract

In recent years, weather forecasting processes have made significant advancements with the increasing power of big data analytics and artificial intelligence (AI) algorithms. The integration of Internet of Things (IoT) technologies has made a substantial contribution to the collection of environmental data and the processing of this data. This study aims to develop weather prediction models by processing weather data collected from IoT sensors using AI-based algorithms. The dataset consists of approximately 600,000 weather data points recorded between specific dates at the weather station established at Fırat University. This data includes various meteorological parameters such as temperature, humidity, pressure, and wind speed. Four different machine learning and deep learning algorithms were used to forecast the weather in the study: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), and XGBoost algorithms. The models were trained on the collected data, and the performance of each algorithm was compared in terms of accuracy rates. The classification results showed that the SVM and KNN models achieved a 98% accuracy rate. The LSTM model reached a 99% accuracy rate, while the highest accuracy rate of 100% was achieved by the XGBoost algorithm. These results demonstrate how different machine learning techniques can contribute to weather forecasting processes and how IoT-derived data can be used. more effectively.

Keywords

Weather monitoring weather station sensor systems raspberry pi artificial intelligence

Project Number

ADEP.23.09

References

  • Aashiq, M. N. M., Kurera, W. T. C. C., Thilekaratne, M. G. S. P., Saja, A. M. A., Rouzin, M. R. M., Neranjan, N., & Yassin, H. (2023). An IoT-based handheld environmental and air quality monitoring station. Acta IMEKO, 12(3), 1-9. https://doi.org/10.21014/actaimeko.v12i3.1487
  • Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine learning applications based on SVM classification a review. Qubahan Academic Journal, 1(2), 81-90. https://doi.org/10.48161/qaj.v1n2a50
  • Ateş, F., & Şenol, R. (2021). Hava Araçlarında Buzlanma Risk Derecesinin Yapay Zeka İle Tahmin Edilmesi. International Journal of 3D Printing Technologies and Digital Industry, 5(3), 457-468. https://doi.org/10.46519/ij3dptdi.957478
  • Banara, S., Singh, T., & Chauhan, A. (2022, January). Iot based weather monitoring system for smart cities: A comprehensive review. In 2022 International Conference for Advancement in Technology (ICONAT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICONAT53423.2022.9726106
  • Çakmak, M. (2024, March). Classification of Apple Quality Using XGBoost Machine Learning Model. In Konya: 4th International Conference on Innovative Academic Studies (pp. 607-615).
  • Dewitte, S., Cornelis, J. P., Müller, R., & Munteanu, A. (2021). Artificial intelligence revolutionises weather forecast, climate monitoring and decadal prediction. Remote Sensing, 13(16), 3209. https://doi.org/10.3390/rs13163209
  • DFRobot - APRS Weather Station Sensor Kit SEN0186. (2023, August 3). APRS Weather Station Sensor Kit SEN0186 - DFRobot. https://wiki.dfrobot.com/APRS_Weather_Station_Sensor_Kit_SEN0186
  • Dogan, T. Yapay Sinir Ağları ve Uyarlanabilir Sinirsel Bulanık Çıkarım Sistemi ile Hava Tahmini. International Journal of Pure and Applied Sciences, 10(1), 12-24. https://doi.org/10.29132/ijpas.1384431
  • Faid, A., Sadik, M., & Sabir, E. (2021). An agile AI and IoT-augmented smart farming: a cost-effective cognitive weather station. Agriculture, 12(1), 35. https://doi.org/10.3390/agriculture12010035
  • Gad, I., & Hosahalli, D. (2022). A comparative study of prediction and classification models on NCDC weather data. International Journal of Computers and Applications, 44(5), 414-425. https://doi.org/10.1080/1206212X.2020.1766769
  • Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and electronics in Agriculture, 155, 41-49. https://doi.org/10.1016/j.compag.2018.09.040
  • Holovatyy, A. (2021). Development of IOT weather monitoring system based on Arduino and ESP8266 Wi-Fi Module. In IOP Conference Series: Materials Science and Engineering (Vol. 1016, No. 1, p. 012014). IOP Publishing. https://doi.org/10.1088/1757-899X/1016/1/012014
  • Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering, 1-19. https://doi.org/10.1007/s11831-021-09622-6
  • Mahmood, S. N., & Hasan, F. F. (2017). Design of weather monitoring system using Arduino based database implementation. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 4(4), 7109.
  • Mohapatra, D., & Subudhi, B. (2022). Development of a cost-effective IoT-based weather monitoring system. IEEE Consumer Electronics Magazine, 11(5), 81-86. https://doi.org/10.1109/MCE.2021.3136833
  • Murugan, K., Tiruveedhi, R. K., Ramireddygari, D. R., Thota, D., & Neeli, C. (2022, December). AI based Weather Monitoring System. In 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) (pp. 1-5). IEEE. https://doi.org/10.1109/ICATIECE56365.2022.10047380
  • Ogunbunmı, S., Taıwo, A. A., Oladosu, J. B., Sanusı, H., Inaolajı, F. A., Olasunkanmı, U. G., ... & Enabulele, E. C. (2024). Internet of things weather monitoring system. World Journal of Advanced Research and Reviews, 22(2), 2099-2110. https://doi.org/10.30574/wjarr.2024.22.2.1647
  • Ren, J., Yu, Z., Gao, G., Yu, G., & Yu, J. (2022). A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism. Energy Reports, 8, 437-443. https://doi.org/10.1016/j.egyr.2022.02.206
  • Schiller, E., Aidoo, A., Fuhrer, J., Stahl, J., Ziörjen, M., & Stiller, B. (2022). Landscape of IoT security. Computer Science Review, 44, 100467. https://doi.org/10.1016/j.cosrev.2022.100467
  • Singh, K. R., Neethu, K. P., Madhurekaa, K., Harita, A., & Mohan, P. (2021). Parallel SVM model for forest fire prediction. Soft Computing Letters, 3, 100014. https://doi.org/10.1016/j.socl.2021.100014
  • Thapa, K. N. K., & Duraipandian, N. (2021). Malicious traffic classification using long short-term memory (LSTM) model. Wireless Personal Communications, 119(3), 2707-2724. https://doi.org/10.1007/s11277-021-08359-6
  • Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 6256. https://doi.org/10.1038/s41598-022-10358-x
  • Üçgün, H., Kaplan, Z.K., & Yüzgeç, U., (2021). Akıllı Hava İstasyonu ile IoT Tabanlı Hava Durumu İzleme Sistemi. European Journal of Science and Technology , (21), 563-571. https://doi.org/10.31590/ejosat.886025
  • Wadud, M. A. H., Kabir, M. M., Mridha, M. F., Ali, M. A., Hamid, M. A., & Monowar, M. M. (2022). How can we manage offensive text in social media-a text classification approach using LSTM-BOOST. International Journal of Information Management Data Insights, 2(2), 100095. https://doi.org/10.1016/j.jjimei.2022.100095
  • Wang, H., Li, G., & Wang, Z. (2023). Fast SVM classifier for large-scale classification problems. Information Sciences, 642, 119136. https://doi.org/10.1016/j.ins.2023.119136
  • Wang, J., Li, J., Wang, X., Wang, J., & Huang, M. (2021). Air quality prediction using CT-LSTM. Neural Computing and Applications, 33, 4779-4792. https://doi.org/10.1007/s00521-020-05535-w
  • Wikipedia - Raspberry Pi 4. (2023, October 5). Raspberry Pi 4 - Wikipedia. https://en.wikipedia.org/wiki/Raspberry_Pi_4
  • Yelgeç, M. A., & Bingöl, O. (2022). Ayrık dalgacık dönüşümü ve XGBoost ile rüzgâr gücü tahmini. Uluslararası Teknolojik Bilimler Dergisi, 14(2), 58-65. https://doi.org/10.55974/utbd.1132336
  • Yeşilyurt, S., & Dalkılıç, H. (2021). XGBoost ve gradient boost machine ile günlük nehir akımı tahmini. In 3rd International Symposium of III Engineering Applications on Civil Engineering and Earth Sciences.
  • Zhang, S. (2021). Challenges in KNN classification. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4663-4675. https://doi.org/10.1109/TKDE.2021.3049250
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723. https://doi.org/10.1109/TKDE.2021.3119140

IOT DESTEKLİ HAVA DURUMU VERİLERİ İLE YAPAY ZEKÂ TABANLI HAVA TAHMİN SİSTEMİNİN GELİŞTİRİLMESİ

Year 2025, Volume: 28 Issue: 1, 524 - 535, 03.03.2025

Nurullah Doğan , Fatih Özyurt

Abstract

Son yıllarda, hava durumu tahmini süreçleri büyük veri analitiği ve yapay zekâ (AI) algoritmalarının artan gücü ile önemli ilerlemeler kaydetmiştir. Özellikle Nesnelerin İnterneti (IoT) teknolojilerinin entegrasyonu, çevresel verilerin toplanması ve bu verilerin işlenmesi süreçlerine büyük katkı sağlamıştır. Bu çalışmada, IoT sensörlerinden toplanan hava durumu verilerinin yapay zekâ temelli algoritmalar ile işlenerek hava tahmin modellerinin geliştirilmesi hedeflenmiştir. Çalışmanın veri seti, Fırat Üniversitesi'nde kurulan hava istasyonunda belirli tarihler arasında toplanan yaklaşık 600.000 adet hava durumu bilgisinden oluşmaktadır. Bu veriler, sıcaklık, nem, basınç, rüzgâr hızı gibi çeşitli meteorolojik parametreleri içermektedir. Çalışmada, dört farklı makine öğrenmesi ve derin öğrenme algoritması kullanılarak hava durumu tahmini yapılmıştır: Destek Vektör Makineleri (SVM), K-En Yakın Komşu (KNN), Uzun Kısa Süreli Bellek (LSTM) ve XGBoost algoritmaları. Modeller, elde edilen verilerle eğitilmiş ve her bir algoritmanın performansı, doğruluk oranları ile karşılaştırılmıştır. Sınıflandırma sonuçları değerlendirildiğinde, SVM ve KNN modelleri %98 doğruluk oranı ile başarılı sonuçlar vermiştir. LSTM modeli ise %99 doğruluk oranına ulaşmış, en yüksek doğruluk oranı ise %100 ile XGBoost algoritması tarafından elde edilmiştir. Bu sonuçlar, farklı makine öğrenmesi tekniklerinin hava tahmini süreçlerine nasıl katkı sağlayabileceğini ve IoT cihazlarından elde edilen verilerin nasıl daha etkili bir şekilde kullanılabileceğini göstermektedir.

Keywords

Hava durumu izleme hava istasyonu sensör sistemleri raspberry pi yapay zekâ

Supporting Institution

Fırat Üniversitesi

Project Number

ADEP.23.09

Thanks

ADEP.23.09 numaralı Bilimsel Araştırma Projesi kapsamındaki desteğinden dolayı Fırat Üniversitesine teşekkür ederiz.

References

  • Aashiq, M. N. M., Kurera, W. T. C. C., Thilekaratne, M. G. S. P., Saja, A. M. A., Rouzin, M. R. M., Neranjan, N., & Yassin, H. (2023). An IoT-based handheld environmental and air quality monitoring station. Acta IMEKO, 12(3), 1-9. https://doi.org/10.21014/actaimeko.v12i3.1487
  • Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine learning applications based on SVM classification a review. Qubahan Academic Journal, 1(2), 81-90. https://doi.org/10.48161/qaj.v1n2a50
  • Ateş, F., & Şenol, R. (2021). Hava Araçlarında Buzlanma Risk Derecesinin Yapay Zeka İle Tahmin Edilmesi. International Journal of 3D Printing Technologies and Digital Industry, 5(3), 457-468. https://doi.org/10.46519/ij3dptdi.957478
  • Banara, S., Singh, T., & Chauhan, A. (2022, January). Iot based weather monitoring system for smart cities: A comprehensive review. In 2022 International Conference for Advancement in Technology (ICONAT) (pp. 1-6). IEEE. https://doi.org/10.1109/ICONAT53423.2022.9726106
  • Çakmak, M. (2024, March). Classification of Apple Quality Using XGBoost Machine Learning Model. In Konya: 4th International Conference on Innovative Academic Studies (pp. 607-615).
  • Dewitte, S., Cornelis, J. P., Müller, R., & Munteanu, A. (2021). Artificial intelligence revolutionises weather forecast, climate monitoring and decadal prediction. Remote Sensing, 13(16), 3209. https://doi.org/10.3390/rs13163209
  • DFRobot - APRS Weather Station Sensor Kit SEN0186. (2023, August 3). APRS Weather Station Sensor Kit SEN0186 - DFRobot. https://wiki.dfrobot.com/APRS_Weather_Station_Sensor_Kit_SEN0186
  • Dogan, T. Yapay Sinir Ağları ve Uyarlanabilir Sinirsel Bulanık Çıkarım Sistemi ile Hava Tahmini. International Journal of Pure and Applied Sciences, 10(1), 12-24. https://doi.org/10.29132/ijpas.1384431
  • Faid, A., Sadik, M., & Sabir, E. (2021). An agile AI and IoT-augmented smart farming: a cost-effective cognitive weather station. Agriculture, 12(1), 35. https://doi.org/10.3390/agriculture12010035
  • Gad, I., & Hosahalli, D. (2022). A comparative study of prediction and classification models on NCDC weather data. International Journal of Computers and Applications, 44(5), 414-425. https://doi.org/10.1080/1206212X.2020.1766769
  • Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation management system using Machine learning and open source technologies. Computers and electronics in Agriculture, 155, 41-49. https://doi.org/10.1016/j.compag.2018.09.040
  • Holovatyy, A. (2021). Development of IOT weather monitoring system based on Arduino and ESP8266 Wi-Fi Module. In IOP Conference Series: Materials Science and Engineering (Vol. 1016, No. 1, p. 012014). IOP Publishing. https://doi.org/10.1088/1757-899X/1016/1/012014
  • Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering, 1-19. https://doi.org/10.1007/s11831-021-09622-6
  • Mahmood, S. N., & Hasan, F. F. (2017). Design of weather monitoring system using Arduino based database implementation. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 4(4), 7109.
  • Mohapatra, D., & Subudhi, B. (2022). Development of a cost-effective IoT-based weather monitoring system. IEEE Consumer Electronics Magazine, 11(5), 81-86. https://doi.org/10.1109/MCE.2021.3136833
  • Murugan, K., Tiruveedhi, R. K., Ramireddygari, D. R., Thota, D., & Neeli, C. (2022, December). AI based Weather Monitoring System. In 2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) (pp. 1-5). IEEE. https://doi.org/10.1109/ICATIECE56365.2022.10047380
  • Ogunbunmı, S., Taıwo, A. A., Oladosu, J. B., Sanusı, H., Inaolajı, F. A., Olasunkanmı, U. G., ... & Enabulele, E. C. (2024). Internet of things weather monitoring system. World Journal of Advanced Research and Reviews, 22(2), 2099-2110. https://doi.org/10.30574/wjarr.2024.22.2.1647
  • Ren, J., Yu, Z., Gao, G., Yu, G., & Yu, J. (2022). A CNN-LSTM-LightGBM based short-term wind power prediction method based on attention mechanism. Energy Reports, 8, 437-443. https://doi.org/10.1016/j.egyr.2022.02.206
  • Schiller, E., Aidoo, A., Fuhrer, J., Stahl, J., Ziörjen, M., & Stiller, B. (2022). Landscape of IoT security. Computer Science Review, 44, 100467. https://doi.org/10.1016/j.cosrev.2022.100467
  • Singh, K. R., Neethu, K. P., Madhurekaa, K., Harita, A., & Mohan, P. (2021). Parallel SVM model for forest fire prediction. Soft Computing Letters, 3, 100014. https://doi.org/10.1016/j.socl.2021.100014
  • Thapa, K. N. K., & Duraipandian, N. (2021). Malicious traffic classification using long short-term memory (LSTM) model. Wireless Personal Communications, 119(3), 2707-2724. https://doi.org/10.1007/s11277-021-08359-6
  • Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 6256. https://doi.org/10.1038/s41598-022-10358-x
  • Üçgün, H., Kaplan, Z.K., & Yüzgeç, U., (2021). Akıllı Hava İstasyonu ile IoT Tabanlı Hava Durumu İzleme Sistemi. European Journal of Science and Technology , (21), 563-571. https://doi.org/10.31590/ejosat.886025
  • Wadud, M. A. H., Kabir, M. M., Mridha, M. F., Ali, M. A., Hamid, M. A., & Monowar, M. M. (2022). How can we manage offensive text in social media-a text classification approach using LSTM-BOOST. International Journal of Information Management Data Insights, 2(2), 100095. https://doi.org/10.1016/j.jjimei.2022.100095
  • Wang, H., Li, G., & Wang, Z. (2023). Fast SVM classifier for large-scale classification problems. Information Sciences, 642, 119136. https://doi.org/10.1016/j.ins.2023.119136
  • Wang, J., Li, J., Wang, X., Wang, J., & Huang, M. (2021). Air quality prediction using CT-LSTM. Neural Computing and Applications, 33, 4779-4792. https://doi.org/10.1007/s00521-020-05535-w
  • Wikipedia - Raspberry Pi 4. (2023, October 5). Raspberry Pi 4 - Wikipedia. https://en.wikipedia.org/wiki/Raspberry_Pi_4
  • Yelgeç, M. A., & Bingöl, O. (2022). Ayrık dalgacık dönüşümü ve XGBoost ile rüzgâr gücü tahmini. Uluslararası Teknolojik Bilimler Dergisi, 14(2), 58-65. https://doi.org/10.55974/utbd.1132336
  • Yeşilyurt, S., & Dalkılıç, H. (2021). XGBoost ve gradient boost machine ile günlük nehir akımı tahmini. In 3rd International Symposium of III Engineering Applications on Civil Engineering and Earth Sciences.
  • Zhang, S. (2021). Challenges in KNN classification. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4663-4675. https://doi.org/10.1109/TKDE.2021.3049250
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723. https://doi.org/10.1109/TKDE.2021.3119140
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence (Other), Software Engineering (Other)
Journal Section Computer Engineering
Authors

Nurullah Doğan 0009-0002-4714-5402

Fatih Özyurt 0000-0002-8154-6691

Project Number ADEP.23.09
Publication Date March 3, 2025
Submission Date August 5, 2024
Acceptance Date November 10, 2024
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Doğan, N., & Özyurt, F. (2025). IOT DESTEKLİ HAVA DURUMU VERİLERİ İLE YAPAY ZEKÂ TABANLI HAVA TAHMİN SİSTEMİNİN GELİŞTİRİLMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 524-535.
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